| Name | daio JSON |
| Version |
0.0.6
JSON |
| download |
| home_page | None |
| Summary | video and data IO tools for Python |
| upload_time | 2025-01-31 13:11:06 |
| maintainer | None |
| docs_url | None |
| author | jlab.berlin |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
python
hdf5
data io
video io
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://opensource.org/licenses/MIT)
[](https://pypi.org/project/daio/)
[](https://anaconda.org/conda-forge/daio)
# daio
Video and data IO tools for Python.
Links: [API documentation](http://danionella.github.io/daio), [GitHub repository](https://github.com/danionella/daio)
## Installation
- via conda or mamba: `conda install conda-forge::daio`
- if you prefer pip: `pip install daio`
- for development, clone this repository change to the directory containing `pyproject.toml`
- `conda env create -n env_name -f environment.yml`
- `conda activate env_name`
- `pip install -e .`
## Use
### Video IO
Write video:
```python
from daio.video import VideoReader, VideoWriter
writer = VideoWriter('/path/to/video.mp4', fps=25)
for i in range(20):
frame = np.random.randint(0,255,size=(720,1280), dtype='uint8')
writer.write(frame)
writer.close()
```
Read video using speed-optimized array-like indexing or iteration:
```python
reader = VideoReader('/path/to/video.mp4')
frame_7 = reader[7]
first10_frames = reader[:10]
for frame in reader:
process_frame(frame)
reader.close()
```
You can also use with statements to handle file closure:
```python
with VideoWriter('/path/to/video.mp4', fps=25) as writer:
for i in range(20):
frame = np.random.randint(0,255,size=(720,1280), dtype='uint8')
writer.write(frame)
#or
with VideoReader('/path/to/video.mp4') as reader:
frame_7 = reader[7]
```
### HDF5 file IO
Lazily load HDF5 with a dict-like interface (contents are only loaded when accessed):
```python
from daio.h5 import lazyh5
h5 = lazyh5('/path/to/datafile.h5')
b_loaded = h5['b']
e_loaded = h5['c']['e']
h5.keys()
```
Create a new HDF5 file (or add items to existing file by setting argument `readonly=False`):
```python
h5 = lazyh5('test.h5')
h5['a'] = 1
h5['b'] = 'hello'
h5['c'] = {} # create subgroup
h5['c']['e'] = [2,3,4]
```
Load entire HDF5-file to dict, or save dict to HDF5-file:
```python
# save dict to HDF5 file:
some_dict = dict(a = 1, b = np.random.randn(3,4,5), c = dict(g='nested'), d = 'some_string')
lazyh5('/path/to/datafile.h5').from_dict(some_dict)
# load dict from HDF5 file:
loaded = lazyh5('/path/to/datafile.h5').to_dict()
```
In Jupyter, you can interactively explore the file structure:
<img width="598" alt="image" src="https://github.com/user-attachments/assets/878d4af6-ce26-4ec3-9ba5-2701fba1c07e">
-----
<details><summary>Old interface (expand this)</summary>
```python
from daio.h5 import save_to_h5, load_from_h5
# save dict to HDF5 file:
some_dict = dict(a = 1, b = np.random.randn(3,4,5), c = dict(g='nested'), d = 'some_string')
save_to_h5('/path/to/datafile.h5', some_dict)
# load dict from HDF5 file:
dict_loaded = load_from_h5('/path/to/datafile.h5')
```
</details>
Raw data
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"description": "\n[](https://opensource.org/licenses/MIT)\n\n[](https://pypi.org/project/daio/)\n[](https://anaconda.org/conda-forge/daio)\n\n\n# daio\nVideo and data IO tools for Python.\n\nLinks: [API documentation](http://danionella.github.io/daio), [GitHub repository](https://github.com/danionella/daio)\n\n## Installation\n- via conda or mamba: `conda install conda-forge::daio`\n- if you prefer pip: `pip install daio`\n- for development, clone this repository change to the directory containing `pyproject.toml` \n - `conda env create -n env_name -f environment.yml`\n - `conda activate env_name`\n - `pip install -e .`\n\n## Use \n\n### Video IO\n\nWrite video:\n```python\nfrom daio.video import VideoReader, VideoWriter\nwriter = VideoWriter('/path/to/video.mp4', fps=25)\nfor i in range(20):\n frame = np.random.randint(0,255,size=(720,1280), dtype='uint8')\n writer.write(frame)\nwriter.close()\n```\n\nRead video using speed-optimized array-like indexing or iteration:\n```python\nreader = VideoReader('/path/to/video.mp4')\nframe_7 = reader[7]\nfirst10_frames = reader[:10]\nfor frame in reader:\n process_frame(frame)\nreader.close()\n```\n\nYou can also use with statements to handle file closure:\n```python\nwith VideoWriter('/path/to/video.mp4', fps=25) as writer:\n for i in range(20):\n frame = np.random.randint(0,255,size=(720,1280), dtype='uint8')\n writer.write(frame)\n#or\nwith VideoReader('/path/to/video.mp4') as reader:\n frame_7 = reader[7]\n```\n\n### HDF5 file IO\n\nLazily load HDF5 with a dict-like interface (contents are only loaded when accessed):\n```python\nfrom daio.h5 import lazyh5\nh5 = lazyh5('/path/to/datafile.h5')\nb_loaded = h5['b']\ne_loaded = h5['c']['e']\nh5.keys()\n```\n\nCreate a new HDF5 file (or add items to existing file by setting argument `readonly=False`):\n```python\nh5 = lazyh5('test.h5')\nh5['a'] = 1\nh5['b'] = 'hello'\nh5['c'] = {} # create subgroup\nh5['c']['e'] = [2,3,4]\n```\n\nLoad entire HDF5-file to dict, or save dict to HDF5-file:\n```python\n# save dict to HDF5 file:\nsome_dict = dict(a = 1, b = np.random.randn(3,4,5), c = dict(g='nested'), d = 'some_string')\nlazyh5('/path/to/datafile.h5').from_dict(some_dict)\n# load dict from HDF5 file:\nloaded = lazyh5('/path/to/datafile.h5').to_dict()\n```\n\nIn Jupyter, you can interactively explore the file structure:\n\n<img width=\"598\" alt=\"image\" src=\"https://github.com/user-attachments/assets/878d4af6-ce26-4ec3-9ba5-2701fba1c07e\">\n\n\n-----\n<details><summary>Old interface (expand this)</summary>\n \n```python\nfrom daio.h5 import save_to_h5, load_from_h5\n# save dict to HDF5 file:\nsome_dict = dict(a = 1, b = np.random.randn(3,4,5), c = dict(g='nested'), d = 'some_string')\nsave_to_h5('/path/to/datafile.h5', some_dict)\n# load dict from HDF5 file:\ndict_loaded = load_from_h5('/path/to/datafile.h5')\n```\n\n</details>\n",
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